no code implementations • 5 Oct 2023 • Jin Jin, Chong Zhang, Jonas Frey, Nikita Rudin, Matias Mattamala, Cesar Cadena, Marco Hutter
In this paper, we model perception failures as invisible obstacles and pits, and train a reinforcement learning (RL) based local navigation policy to guide our legged robot.
1 code implementation • 25 Apr 2022 • Xirui Hou, Pengfei Guo, Puyang Wang, Peiying Liu, Doris D. M. Lin, Hongli Fan, Yang Li, Zhiliang Wei, Zixuan Lin, Dengrong Jiang, Jin Jin, Catherine Kelly, Jay J. Pillai, Judy Huang, Marco C. Pinho, Binu P. Thomas, Babu G. Welch, Denise C. Park, Vishal M. Patel, Argye E. Hillis, Hanzhang Lu
Deep-learning resting-state vascular imaging has the potential to become a useful tool in clinical cerebrovascular imaging.
no code implementations • 16 Jul 2021 • Francesco Cognolato, Kieran O'Brien, Jin Jin, Simon Robinson, Frederik B. Laun, Markus Barth, Steffen Bollmann
NeXtQSM offers a new deep learning based pipeline for computing quantitative susceptibility maps that integrates each processing step into the training and provides results that are robust and fast.
1 code implementation • 10 Aug 2020 • Changhong Fu, Fangqiang Ding, Yiming Li, Jin Jin, Chen Feng
By repressing the response of distractors in the regressor learning, we can dynamically and adaptively alter our regression target to leverage the tracking robustness as well as adaptivity.
1 code implementation • 10 Aug 2020 • Fangqiang Ding, Changhong Fu, Yiming Li, Jin Jin, Chen Feng
Current unmanned aerial vehicle (UAV) visual tracking algorithms are primarily limited with respect to: (i) the kind of size variation they can deal with, (ii) the implementation speed which hardly meets the real-time requirement.
1 code implementation • 2 Jul 2020 • Jin Jin, Lin Zhang, Ethan Leng, Gregory J. Metzger, Joseph S. Koopmeiners
While current research has shown the importance of Multi-parametric MRI (mpMRI) in diagnosing prostate cancer (PCa), further investigation is needed for how to incorporate the specific structures of the mpMRI data, such as the regional heterogeneity and between-voxel correlation within a subject.
1 code implementation • 7 Feb 2020 • Jin Jin, Marie-Karelle Riviere, Xiaodong Luo, Yingwen Dong
We did simulation studies to compare CBHM with independent analysis and three information borrowing approaches: the conventional Bayesian hierarchical model, the EXNEX approach and Liu's two-stage approach.
Applications
1 code implementation • 19 Nov 2019 • Shahrokh Abbasi-Rad, Kieran O'Brien, Samuel Kelly, Viktor Vegh, Anders Rodell, Yasvir Tesiram, Jin Jin, Markus Barth, Steffen Bollmann
Purpose: The purpose of this study is to demonstrate a method for Specific Absorption Rate (SAR) reduction for T2-FLAIR MRI sequences at 7T by predicting the required adiabatic pulse power and scaling the amplitude in a slice-wise fashion.